Global variance equalization for improving deep neural network based speech enhancement

We address an over-smoothing issue of enhanced speech in deep neural network (DNN) based speech enhancement and propose a global variance equalization framework with two schemes, namely post-processing and post-training with modified object function for the equalization between the global variance of the estimated and the reference speech. Experimental results show that the quality of the estimated clean speech signal is improved both subjectively and objectively in terms of perceptual evaluation of speech quality (PESQ), especially in mismatch environments where the additive noise is not seen in the DNN training.

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